Seaborn : Statistical Data Visualization¶

Numerical Data Plotting¶

relpolt()
scatterplot()
lineplot()

Categorical Data Plotting¶

catplot()
boxplot()
stripplot()
swarmplot()
etc

Visualizing Distribution of Data¶

histplot()/distplot()
kdeplot()
jointplot()
rugplot()

Linear Regression and Relationship¶

regplot()
impolt()

Loading Libraries¶

In [3]:

import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

Numerical Data Plotting¶

Relplot() : kind = scatter¶

Loading Dataset¶

In [41]:

tips = sns.load_dataset('tips')
tips.head()

Out[41]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

In [26]:

sns.relplot(x = 'total_bill', y = 'tip', data = tips )

sns.set_style('darkgrid')

plt.show()

In [44]:

sns.relplot(x = 'total_bill', y = 'tip', data = tips, hue = 'smoker')

sns.set_style('white')

plt.show()

In [46]:

sns.relplot(x = 'total_bill', y = 'tip', data = tips, hue = 'size', size = 'size')

plt.show()

In [58]:

sns.relplot(x = 'total_bill', y = 'tip', data = tips, hue = 'size', palette = 'ch:r = -0.8, l = 0.9')

plt.show()

In [61]:

sns.relplot(x = 'total_bill', y = 'tip', data = tips, hue = 'sex', style = 'time')

plt.show()

In [67]:

sns.relplot(x = 'total_bill', y = 'tip', data = tips, size = 'size', sizes = (50, 200), hue = 'size')

plt.show()

Relplot() : kind = line¶

One Line Plot¶

Generating a Random Dataset¶

This dataset is sorted along the column 'time'.

In [4]:

from numpy.random import randn
df = pd.DataFrame(dict(time = np.arange(500), value = randn(500).cumsum()))
df.head()

Out[4]:

	time	value
0	0	-1.646790
1	1	0.516107
2	2	-0.921064
3	3	0.230848
4	4	-1.095119

In [75]:

sns.relplot(x = 'time', y = 'value', data = df, kind = 'line')
plt.show()

This dataset is not sorted along any columns

In [81]:

df2 = pd.DataFrame(randn(500,2).cumsum(axis = 0), columns = ['time', 'value'])
df2.head()

Out[81]:

	time	value
0	-0.614219	-1.085370
1	-1.384408	-1.888193
2	-2.420642	-2.347025
3	-2.973640	-2.065168
4	-2.012564	-3.020315

In [86]:

sns.relplot(x = 'time', y = 'value', data = df2, kind = 'line', sort = True)
plt.show()

In [89]:

sns.relplot(x = 'time', y = 'value', data = df2, kind = 'line', sort = False)
plt.show()

This dataset is has multiple values for the same 'timepoint'

In [10]:

fmri = sns.load_dataset('fmri')
fmri.head()

Out[10]:

	subject	timepoint	event	region	signal
0	s13	18	stim	parietal	-0.017552
1	s5	14	stim	parietal	-0.080883
2	s12	18	stim	parietal	-0.081033
3	s11	18	stim	parietal	-0.046134
4	s10	18	stim	parietal	-0.037970

In [98]:

#the shaded region is confidence interval, the bold line shows 95% confidence in the value 
sns.relplot(x = 'timepoint', y = 'signal', kind = 'line', data = fmri)
plt.show()

In [96]:

#we can turn off the ci by ci = False
sns.relplot(x = 'timepoint', y = 'signal', kind = 'line', data = fmri, ci = False)
plt.show()

In [99]:

#we can show the standar deviation too
sns.relplot(x = 'timepoint', y = 'signal', kind = 'line', data = fmri, ci = 'sd')
plt.show()

In [106]:

# estimator = None lots the real data set without ci 
sns.relplot(x = 'timepoint', y = 'signal', estimator = None, kind = 'line', data = fmri)
plt.show()

Two Line Plot¶

In [112]:

sns.relplot(x = 'timepoint', y = 'signal', hue = 'event' , kind = 'line', data = fmri)
plt.show()

In [116]:

sns.relplot(x = 'timepoint', y = 'signal', style = 'event', hue = 'region', kind = 'line', data = fmri, ci = False)
plt.show()

In [120]:

#adding markers
sns.relplot(x = 'timepoint', y = 'signal', style = 'event', hue = 'region', kind = 'line',
            data = fmri, ci = False, markers = True, dashes = False)
plt.show()

In [125]:

sns.relplot(x = 'timepoint', y = 'signal', style = 'event', hue = 'event', kind = 'line', data = fmri)
plt.show()

In [128]:

#query() helps to segregate the data
#units helps to plot multiple subjects in one plot
sns.relplot(x = 'timepoint', y = 'signal', kind = 'line', data = fmri.query("event == 'stim'"), hue = 'region', 
            units = 'subject', estimator = None)
plt.show()

Changing default Sizes and Colors of the plots¶

Creating a palette¶

In [31]:

dots = sns.load_dataset('dots').query("align == 'dots'")
dots.head()

Out[31]:

	align	choice	time	coherence	firing_rate
0	dots	T1	-80	0.0	33.189967
1	dots	T1	-80	3.2	31.691726
2	dots	T1	-80	6.4	34.279840
3	dots	T1	-80	12.8	32.631874
4	dots	T1	-80	25.6	35.060487

In [134]:

sns.relplot(x = 'time', y = 'firing_rate', data = dots, kind = 'line')
plt.show()

In [135]:

sns.relplot(x = 'time', y = 'firing_rate', data = dots, kind = 'line', hue = 'coherence')
plt.show()

In [137]:

#as the hue colors are quite indistinguishable, we have to create a palette of our own
sns.relplot(x = 'time', y = 'firing_rate', data = dots, kind = 'line', hue = 'coherence', style = 'choice')
plt.show()

In [162]:

#palette is created by cubehelix
palette = sns.cubehelix_palette(light = 0.8, n_colors = 6)
palette

Out[162]:

In [153]:

#now the hues are more distinguishable
sns.relplot(x = 'time', y = 'firing_rate', data = dots, kind = 'line', hue = 'coherence', 
            style = 'choice', palette = palette)
plt.show()

Changing sizes of lines¶

In [171]:

sns.relplot(x = 'time', y = 'firing_rate', data = dots, kind = 'line', size = 'choice', style = 'coherence', 
            sizes = (2,4))
plt.show()

Plotting with Date-Time¶

Creating a Dataset with Date values

In [176]:

time = pd.DataFrame(dict(time = pd.date_range('2020-08-02', periods = 500), value = randn(500).cumsum()))
time.head()

Out[176]:

	time	value
0	2020-08-02	0.046703
1	2020-08-03	-0.004820
2	2020-08-04	-1.465448
3	2020-08-05	-1.532988
4	2020-08-06	-1.353086

In [182]:

g = sns.relplot(x = 'time', y = 'value', kind = 'line', data = time)
#formats the date to fit the plot canvas
g.fig.autofmt_xdate()

Creating Multi-Plots in the Same Canvas¶

In [183]:

tips = sns.load_dataset('tips')
tips.head()

Out[183]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

In [206]:

sns.relplot(x = 'total_bill', y = 'tip', data = tips, hue = 'smoker', row = 'day')
plt.show()

In [205]:

sns.relplot(x = 'total_bill', y = 'tip', data = tips, hue = 'smoker', col = 'day')
plt.show()

In [188]:

sns.relplot(x = 'total_bill', y = 'tip', data = tips, hue = 'smoker', col = 'time')
plt.show()

In [196]:

sns.relplot(x = 'timepoint', y = 'signal',hue = 'subject', col = 'region', row = 'event', height = 4, kind = 'line',
            data = fmri)
plt.show()

In [7]:

#col_wrap defines the no. of subplots thatare going to be simultaneously in line
sns.relplot(x = 'total_bill', y = 'tip', data = tips, hue = 'smoker', col = 'day', col_wrap = 2, height = 3)
plt.show()

Lineplot()¶

In [8]:

sns.lineplot(x = 'total_bill', y = 'tip', data = tips)
plt.show()

In [51]:

#err_style shows the range of errors
sns.lineplot(x = 'timepoint', y = 'signal', data = fmri, hue = 'region', style = 'event',
             err_style = 'bars')
plt.legend(bbox_to_anchor = (1.05, 1.025), loc = 'upper left')
plt.show()

In [50]:

sns.lineplot(x = 'timepoint', y = 'signal', data = fmri.query("region == 'frontal'"), hue = 'event', 
             units = 'subject', estimator = None, lw = 1)
plt.legend(bbox_to_anchor = (1.05, 1.025), loc = 'upper left')

plt.show()

In [45]:

sns.lineplot(x = 'time', y = 'firing_rate', hue = 'coherence', style = 'choice', data = dots)
#putting legend outside of pot
plt.legend(bbox_to_anchor = (1.05, 1.025), loc = 'best')

plt.show()

Scatterplot()¶

In [44]:

sns.scatterplot(x= 'total_bill', y = 'tip', data = tips, hue = 'smoker', size = 'size')
plt.legend(bbox_to_anchor = (1.05, 1.025), loc = 'best')
plt.show()

In [68]:

iris = sns.load_dataset('iris')
iris.head()

Out[68]:

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

In [55]:

sns.scatterplot(x= 'sepal_length', y= 'petal_width', data = iris)
plt.show()

Categorical Data Plotting¶

Catplot()¶

Loading Dataset¶

In [56]:

titanic = sns.load_dataset('titanic')
titanic.head()

Out[56]:

	survived	pclass	sex	age	sibsp	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

Stripplot()¶

In [92]:

sns.catplot(x= 'day', y = 'total_bill', data = tips)
#adding title to the plot
plt.title('stripplot()', fontdict = {'fontname': 'Times New Roman', 'color': 'grey', 'fontsize' : '26'})
plt.show()

In [70]:

sns.catplot(y = 'day', x = 'total_bill', data = tips)
plt.show()

In [72]:

#jitter concentrates all the dots into one line
sns.catplot(x = 'day', y = 'total_bill', data = tips, jitter = False)
plt.show()

In [77]:

#reordering the axis labels
sns.catplot(x = 'smoker', y = 'tip', data = tips, order = ['No', 'Yes'])
plt.show()

Swarmplot()¶

In [74]:

#swarm stops the overlapping of the dots
sns.catplot(x = 'day', y = 'total_bill', data = tips, kind = 'swarm')
plt.show()

In [75]:

sns.catplot(x = 'day', y = 'total_bill', data = tips, kind = 'swarm', hue = 'sex')
plt.show()

In [78]:

sns.catplot(x = 'day', y = 'total_bill', data = tips, kind = 'swarm', hue = 'size')
plt.show()

Boxplot()¶

In [83]:

sns.catplot(x  ='day', y= 'total_bill', kind = 'box', data = tips)
plt.show()

In [84]:

sns.catplot(x  ='day', y= 'total_bill', kind = 'box', data = tips, hue ='sex')
plt.show()

In [90]:

sns.catplot(x  ='day', y= 'total_bill', kind = 'box', data = tips, hue ='sex', dodge = False)
plt.show()

In [88]:

sns.catplot(x  ='day', y= 'total_bill', kind = 'box', data = tips, hue ='time')
plt.show()

In [89]:

sns.catplot(x  ='day', y= 'total_bill', kind = 'box', data = tips, hue ='time', dodge = False)
plt.show()

In [91]:

diamonds = sns.load_dataset('diamonds')
diamonds.head()

Out[91]:

	carat	cut	color	clarity	depth	table	price	x	y	z
0	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
1	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
2	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
3	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
4	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

Boxenplot()¶

In [105]:

sns.catplot(x = 'color', y = 'price', data = diamonds, kind= 'boxen')
plt.show()

In [103]:

sns.catplot(x  ='day', y= 'total_bill', kind = 'boxen', data = tips, dodge = False)
sns.set_style('whitegrid')
plt.show()

Violinplot()¶

In [107]:

sns.catplot(y = 'total_bill', x = 'day', hue = 'time', data = tips, kind = 'violin')
plt.show()

In [108]:

sns.catplot(y = 'total_bill', x = 'day', hue = 'sex', data = tips, kind = 'violin')
plt.show()

In [109]:

sns.catplot(y = 'total_bill', x = 'day', hue = 'sex', data = tips, kind = 'violin', split= True)
plt.show()

In [110]:

sns.catplot(y = 'total_bill', x = 'day', hue = 'sex', data = tips, kind = 'violin', split= True, inner = 'stick')
plt.show()

Violinplot() & Swarmplot()¶

In [116]:

v = sns.catplot(y = 'total_bill', x = 'day', data = tips, kind = 'violin', inner = None)
sns.swarmplot(x= 'day', y = 'total_bill', data = tips , ax = v.ax, color = 'k')

Out[116]:

<AxesSubplot:xlabel='day', ylabel='total_bill'>

Barplot()¶

In [119]:

sns.catplot(x = 'sex', y = 'survived', data = titanic, hue = 'class', kind = 'bar')
plt.show()

Boxplot() & Countplot()¶

In [121]:

sns.catplot(x = 'deck', data = titanic, kind = 'count')
plt.show()

In [122]:

sns.catplot(x = 'deck', data = titanic, kind = 'count', hue = 'class')
plt.show()

Pointplot()¶

In [124]:

sns.catplot(x = 'sex', y = 'survived', data = titanic, hue = 'class', kind = 'point')
plt.show()

Visualizing Distribution of Data¶

Histplot()¶

In [12]:

x = randn(100)
x

Out[12]:

array([ 1.03849761, -0.37672645, -1.43153686, -0.22816897,  1.81184077,
        0.38639951,  0.53608913, -0.18621981,  0.71423295, -0.61659147,
       -1.8110595 ,  0.84925892, -0.46794901,  0.60218716, -1.6764701 ,
        0.65032625, -0.08736699,  0.99687418,  0.66530761, -0.91549972,
       -0.19320073,  1.22425756,  0.95669515,  0.60678554,  0.20314522,
       -0.64208729,  0.50985479, -0.70023906,  0.03509268, -0.40293357,
        1.73259479, -0.0396044 , -0.11178577, -0.68514119, -0.28888548,
       -0.91279263, -0.08474102, -0.66675444, -0.62544381, -0.20116449,
        1.11445034,  1.33389295,  0.17959006, -0.01036273,  2.39500859,
        0.11992822,  0.27085085, -0.73093507, -0.28344056, -0.73020751,
        0.33993413,  1.05009823,  0.10055788, -0.22488009,  1.68049799,
        0.81219463,  0.50441191, -0.79379266,  1.1406055 ,  1.13813026,
       -0.74574608, -1.64367362, -0.01813715, -0.67425043, -1.92620524,
       -0.54931383,  0.28912791,  1.5635013 , -0.41319878,  1.85296273,
        1.84101099,  0.38402611,  0.83401404, -1.00296171, -0.13528173,
        0.30715852,  0.73478121,  1.03018871, -0.74850326, -2.18186893,
       -1.23103429,  0.30084036, -0.64598564, -0.42614503,  0.34573588,
        0.23991899, -0.93564578, -0.2720147 , -0.12454321, -0.04094848,
        0.30514334, -0.45848864, -0.60815485, -0.25252027, -1.76714445,
        0.50266259,  0.75699161, -0.6630731 , -1.52461079, -0.08192615])

In [19]:

sns.histplot(x, kde = False)
plt.show()

In [23]:

sns.histplot(x, kde = True)
plt.show()

In [24]:

sns.histplot(x, kde = True, bins = 20)
plt.show()

Kdeplot()¶

In [35]:

sns.kdeplot(x, shade = True)
plt.show()

In [38]:

# cut tapers the curve line
sns.kdeplot(x, shade = True, cut = 10)
plt.show()

In [39]:

# bw_adjust smoothens the curve line
sns.kdeplot(x, shade = True, bw_adjust = 2)
plt.show()

In [66]:

f, ax = plt.subplots(figsize = (6,6))
cmap = sns.cubehelix_palette(as_cmap = True, dark = 0 ,light = 1, reverse = True)
sns.kdeplot(x = 'total_bill', y = 'tip', data = tips, cmap = cmap, n_levels = 100, shade = True)
plt.show()

Jointplot()¶

In [42]:

sns.jointplot(x = 'total_bill', y = 'tip', data = tips)
plt.show()

In [46]:

sns.jointplot(x = 'total_bill', y = 'tip', data = tips, kind = 'hex')
plt.show()

In [47]:

sns.jointplot(x = 'total_bill', y = 'tip', data = tips, kind = 'kde')
plt.show()

Pairplot()¶

In [70]:

sns.pairplot(iris)
plt.show()

In [74]:

g = sns.pairplot(iris)
g.map_diag(sns.kdeplot)
g.map_offdiag(sns.kdeplot)
plt.show()

Linear Regression and Relationship¶

Regplot()¶

In [82]:

sns.regplot(x = 'total_bill', y = 'tip', data = tips)
plt.show()

Lmplot()¶

In [83]:

sns.lmplot(x = 'total_bill', y = 'tip', data = tips)
plt.show()

In [84]:

sns.lmplot(x = 'size', y = 'tip', data = tips)
plt.show()

In [86]:

sns.lmplot(x = 'size', y = 'tip', data = tips, x_jitter = 0.1)
plt.show()

In [88]:

sns.lmplot(x = 'size', y = 'tip', data = tips, x_estimator = np.mean)
plt.show()

In [89]:

sns.lmplot(x = 'size', y = 'tip', data = tips, x_estimator = np.median)
plt.show()

In [93]:

ans = sns.load_dataset('anscombe')
ans.head()

Out[93]:

	dataset	x	y
0	I	10.0	8.04
1	I	8.0	6.95
2	I	13.0	7.58
3	I	9.0	8.81
4	I	11.0	8.33

In [95]:

ans['dataset'].value_counts()

Out[95]:

I      11
II     11
III    11
IV     11
Name: dataset, dtype: int64

In [101]:

sns.lmplot(x = 'x', y = 'y', data = ans.query("dataset == 'I'"), ci = None, scatter_kws = {'s':80})
plt.show()

In [102]:

sns.lmplot(x = 'x', y = 'y', data = ans.query("dataset == 'II'"), ci = None, scatter_kws = {'s':80})
plt.show()

In [105]:

sns.lmplot(x = 'x', y = 'y', data = ans.query("dataset == 'II'"), ci = None, scatter_kws = {'s':80}, order = 2)
plt.show()

In [106]:

sns.lmplot(x = 'x', y = 'y', data = ans.query("dataset == 'III'"), ci = None, scatter_kws = {'s':80})
plt.show()

In [124]:

sns.lmplot(x = 'total_bill', y = 'tip', data = tips, hue = 'sex', markers = ['o', '*'])
#despine is used to remove axes for a cleaner look
sns.despine(left = True, bottom = True)
plt.show()

In [117]:

sns.lmplot(x = 'total_bill', y = 'tip', data = tips, hue = 'sex', markers = ['o', '*'], col = 'time', row = 'smoker')
plt.show()

In [120]:

sns.lmplot(x = 'total_bill', y = 'tip', data = tips, col = 'day', col_wrap = 2)
plt.show()

Seaborn : Statistical Data Visualization¶

Numerical Data Plotting¶

Categorical Data Plotting¶

Visualizing Distribution of Data¶

Linear Regression and Relationship¶

Loading Libraries¶

Numerical Data Plotting¶

Relplot() : kind = scatter¶

Loading Dataset¶

Relplot() : kind = line¶

One Line Plot¶

Generating a Random Dataset¶

Two Line Plot¶

Changing default Sizes and Colors of the plots¶

Creating a palette¶

Changing sizes of lines¶

Plotting with Date-Time¶

Creating Multi-Plots in the Same Canvas¶

2x2 Facet Grid¶

Lineplot()¶

Scatterplot()¶

Categorical Data Plotting¶

Catplot()¶

Loading Dataset¶

Stripplot()¶

Swarmplot()¶

Boxplot()¶

Boxenplot()¶

Violinplot()¶

Violinplot() & Swarmplot()¶

Barplot()¶

Boxplot() & Countplot()¶

Pointplot()¶

Visualizing Distribution of Data¶

Histplot()¶

Kdeplot()¶

Jointplot()¶

Pairplot()¶

Linear Regression and Relationship¶

Regplot()¶

Lmplot()¶